Raw Data

Using Emotiv headsets, we have access to multiple data output formats, including: csv, edf, edf+, bdf, etc. EDF+ files have become the defacto for EEG data. They are more memory efficient than CSV files, allowing faster computations with less resources, and they include categories for all of the raw data from our Emotiv eeg headset.

In order to begin working with our data, we must import it! We have several methods available. Let's start with the most simple example, we'll open one file and plot the raw data:

import mne
  
  raw = mne.io.read_raw_edf(r'EDF+\103918\103918.edf') # raw = mne.io.read_raw_edf(r'EDF+\103918\103918.edf', eog=['fp1', 'fp2'], preload=True, infer_types=True)
  raw.plot(block=True) #Block must be True if working interactively with a .py file
  

Extracting EDF parameters from f:\WEBSITE\EDF_analysis\EDF+\103918\103918.edf...
  EDF file detected
  Setting channel info structure...
  Creating raw.info structure...
  

C:\Users\Admin\AppData\Local\Temp\ipykernel_16460\66295739.py:4: RuntimeWarning: Loading an EDF with mixed sampling frequencies and preload=False will result in edge artifacts. It is recommended to use preload=True.See also https://github.com/mne-tools/mne-python/issues/10635
    raw.plot(block=True) #Block must be True if working interactively with a .py file
  

Refining the data on import

We can see that the above plots all of the data! Helpful to understand what we're dealing with. We can see that we're plotting data which does not correspond to eeg electrode locations! We can work with the data more closely upon input and help mne sort out the data

import mne
  
  # Define eeg channels for when we need to pass them explicitly as arguments
  eeg_channels = ['Cz', 'Fz', 'Fp1', 'F7', 'F3', 'FC1', 'C3', 'FC5', 'FT9', 'T7', 'CP5', 'CP1', 'P3', 'P7', 'PO9', 'O1', 'Pz', 'Oz', 'O2', 'PO10', 'P8', 'P4', 'CP2', 'CP6', 'T8', 'FT10', 'FC6', 'C4', 'FC2', 'F4', 'F8', 'Fp2']
  eog_channels=['Fp1', 'Fp2']
  
  
  #raw = mne.io.read_raw_edf(r'EDF+\103918\103918.edf', eog=['Fp1', 'Fp2'], preload=True, infer_types=True) ##NOTE: YOU MUST watch the capitalization!
  raw = mne.io.read_raw_edf(
      r'EDF+\103918\103918.edf',
      #eog=['Fp1', 'Fp2'], ###NOTE: YOU MUST watch the capitalization!Define eog channels!  In the future, I suggest trying making a copy of these as eog1 and eog2
      misc=None, # List of channel names to be considered as miscellaneous (MISC) channels.
      stim_channel=None,  # Set to None if you don't have a stim channel
      exclude=[],  # Exclude channels you don't want
      preload=True,  # Preload data into memory to speed things up
      infer_types=True,  # Infer channel types from names
      verbose=True  # Set verbosity / output messages
      )
  
  raw.pick('eeg') # Pick only eeg related data!  However, we still have more data then we need!  We'll plot only the channels we want
  raw.plot(picks=eeg_channels, block=True) #Block must be True if working interactively with a .py file.  Also, we are picking only eeg channels
  
  raw.pick('eeg').plot(block=True) # Pick only eeg related data!  However, we still have more data then we need!  We'll plot only the channels we want
  # raw #Block must be True if working interactively with a .py file.  Also, we are picking only eeg channels
  

Extracting EDF parameters from f:\WEBSITE\EDF_analysis\EDF+\103918\103918.edf...
  EDF file detected
  Setting channel info structure...
  Creating raw.info structure...
  Reading 0 ... 143999  =      0.000 ...  1124.992 secs...
  

After importing the data we can move to preprocessing and filtering. Go to pre-processing